Since professor L. A. Zadeh of the University of California presented the fuzzy theory and its applications in "Journal of Information and Control" in 1965, research and development for practical applications of fuzzy control fuzzy computers and fuzzy artificial intelligence in which the fuzzy theory is employed have been ongoing.
Fuzzy control expresses control algorithms by using an "if . . . then" format (fuzzy control routine). A fuzzy computer executes the algorithms by using fuzzy inference in order to measure the senses of a human or the ambiguity of a word such as, for example: "knack": that which is obtained from a long period of experience of those skilled workers (expert) in a specific field.
That is ambiguous word information corresponding, for example, to "slow", "medium" and "quick", as used to describe a speed, is expressed by respective membership functions. One fact is verified by the respective fuzzy rules of an "if . . . then" format to check its approximate agreement. A membership function of the consequent section "then" is cut by the agreement of the antecedent section "if" of the above-mentioned rule, and after respective inference results are obtained, an essence is extracted from all the inference results consisting of the ambiguous information (this is called defuzzification).
Numerous defuzzification methods have been proposed. However, in practice, a center-of-gravity method is most widely used.
Next, let's consider a computer which performs fuzzy inference (here, this is tentatively called a "fuzzy computer"). Information handled by a conventional digital computer is all definite information expressed by binary information (binary words of a combination of 0 and 1). A fuzzy computer, however, must handle information specified by a membership function for each ambiguous word information. Hence, a fuzzy computer must process a great amount of information expressed by decimals, for example, 0., 0.1, 0.2, 0.3, . . . in grades from 0 to 1 with respect to respective membership functions, concerning a word to be processed (this is tentatively called a "fuzzy word").
Although a fuzzy computer handles ambiguous word information such as "slow", "quicker" and so forth, a "fact" (input information) of the inference executed by a fuzzy logic operation circuit in a fuzzy computer and output information are definite values (e.g., 15.degree., 5 V, etc.). Accordingly, if this input and output information cannot be processed at high speed, even if fuzzy inference in execution in the fuzzy computer is performed at high speed, its processing is limited greatly.
Even after professor Mamdani of London University presented in 1974 the first expert system by means of fuzzy control in which fuzzy theory is applied (fuzzy control for a steam engine), the history of fuzzy control technology is still short. It has not been until recently that some full-fledged expert systems with highly rated advantages have been realized.
In the execution of fuzzy inference for fuzzy control, it has been found in the art that the inference operation may be completed faster by utilizing dedicated hardware (i.e. a digital computer). Accordingly, the speed from the time a "fact" is input to the time the result of its inference is displayed on a display section is limited by the processing performance of the above-mentioned digital computer. As a result, fuzzy logic operation circuits exclusively used for a fuzzy computer have been expected which are capable of effectively performing not only input and output of fuzzy information but also the very fuzzy logic operations themselves.
A method of directly mapping the current state quantity of devices to control quantity via digital memory has been proposed. The method has the possibility of reducing logic operation time remarkably. Fine adjustments of the parameters are, however, difficult. In addition, analog fuzzy information processing chips composed of a combination of a number of operational amplifications and so forth have now been developed, but they are not sufficient in logic operation, speed or processing performance.
Charge transfer type devices represented by CCDs are comparatively new Si devices announced by Boyle in 1970 and utilize minority carriers and dynamic electric-field effects. The devices have been developed considerably by novel technical concepts such that functional devices are constituted by charge transfer and the use of LSI technology. By using the properties of CCDs, image pickup devices, large capacity memories, analog signal processing, and numerous kinds of filters, including matched filters, delay lines and so forth, have been put to practical use. However, at present, they are not used to any degree in a high-level information processing apparatus such as a fuzzy computer.
An object of the present invention is to provide a basic fuzzy logic operation circuit in which the properties of the CCDs, resulting from a charge transfer function, are employed to provide the following multi-functionality: analog memory; direct handling of an analog quantity; low power consumption; low noise; and an economical fuzzy computer using the circuit.
The minimum functions necessary for a fuzzy operation can be realized by the following two kinds of basic functions and their combination because of the properties of a well-known "fuzzy inference engine" (e.g., architecture in which A and B as a knowledge and A' as a fact are input and B' is output as a conclusion), (for details, see "Concept of a Fuzzy Computer", by Retsu Yamakawa, published in Aug. 19, 1988, Kodansha Publishing Co.). The minimum functions are summarized as follows:
i) a function to select a maximum or minimum quantity of information from among a plurality of fuzzy information and output it, and
ii) a function capable of determining its representative value for a plurality of ranked fuzzy information.
The present invention comprises basic fuzzy logic operation circuit devices and a defuzzifier using CCDs and a fuzzy computer composed of a number of the above-mentioned fuzzy logic operation circuit devices and connected with the above-mentioned defuzzifier.
Since a basic fuzzy logic operation circuit device and defuzzifier having AND and OR functions are constructed by using CCDs, a high-speed fuzzy computer exclusively used for fuzzy control can be realized. This is done by connecting as many of the above-mentioned circuit devices in parallel as there are numbers of fuzzy variables and connecting the above-mentioned defuzzifier to its output side.